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Discovering Communities in Linked Data by Multi-view Clustering

  • Isabel Drost
  • Steffen Bickel
  • Tobias Scheffer
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

We consider the problem of finding communities in large linked networks such as web structures or citation networks. We review similarity measures for linked objects and discuss the k-Means and EM algorithms, based on text similarity, bibliographic coupling, and co-citation strength. We study the utilization of the principle of multi-view learning to combine these similarity measures. We explore the clustering algorithms experimentally using web pages and the Cite-Seer repository of research papers and find that multi-view clustering effectively combines link-based and intrinsic similarity.

Keywords

Citation Analysis Citation Network Cluster Quality Bibliographic Coupling True Class Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Isabel Drost
    • 1
  • Steffen Bickel
    • 1
  • Tobias Scheffer
    • 1
  1. 1.Institut für InformatikHumboldt-Universität zu BerlinBerlinGermany

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